How Robust Is the Wisdom of the Crowds?
Authors: Noga Alon, Michal Feldman, Omer Lev, Moshe Tennenholtz
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper primarily focuses on theoretical proofs and analysis of graph properties (e.g., Theorem 1, Theorem 2, Theorem 3, Proposition 1) to establish bounds and robustness conditions for social networks. It uses examples to illustrate theoretical concepts but does not involve empirical data collection, experimentation, or performance metrics. |
| Researcher Affiliation | Collaboration | Noga Alon Tel Aviv University and Microsoft Research, Israel nogaa@tau.ac.il Michal Feldman Tel Aviv University and Microsoft Research, Israel michal.feldman@cs.tau.ac.il Omer Lev Hebrew University and Microsoft Research, Israel omerl@cs.huji.ac.il Moshe Tennenholtz Technion, Israel moshet@ie.technion.ac.il |
| Pseudocode | No | The paper presents mathematical proofs and theoretical examples, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper relies on theoretical models of graphs (e.g., clique, star network, expander graphs, random graphs G=G(n,p)) rather than empirical datasets. There is no mention of publicly available datasets for training, as it is a theoretical work. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits (training, validation, test). Therefore, no validation split information is provided. |
| Hardware Specification | No | The paper does not mention any specific hardware used for computational work, as it is a theoretical study. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup with hyperparameters or system-level training settings. |